在這份投影片中，首先介紹商業智慧簡介及企業內部數據決策的流程，並使用一個例子呈現從數據處理到資料視覺化的實作。 讓對資料科學及人工智慧有興趣的同好了解資料處理及商業智慧的應用。

1. 從數據處理到資料視覺化—
商業智慧的實作與運用
OPEN LIVING
樂活共生實驗基地
» Pei-Syuan
» 2018/12/8

2. Outline
» Business Intelligence
» 企業內部數據決策
» 呈現視覺化報表的步驟
» R
» Power BI
» Data Cleaning
» Data Preprocessing
» Data Visualization
2

3. Business Intelligence
3
 Source:
http://www.jandhconsulting.net/business-intelligence-bi-en.html

4. 企業內部數據決策
4
數據處理
<-數據處理
 Source:
https://medium.com/@Echo.C/%E4%B8%80%E5%80%8B%E9%97%9C%E6%96%BC%E6%A3
%97%E5%AD%90%E6%A0%B8%E6%A1%83%E7%9A%84-meetup-
%E5%88%86%E4%BA%AB-b5c55b78421f

5. What I am doing now
5
數據處理IT: 資料庫
 Source:
https://medium.com/@Echo.C/%E4%B8%80%E5%80%8B%E9%97%9C%E6%96%BC%E6%A3
%97%E5%AD%90%E6%A0%B8%E6%A1%83%E7%9A%84-meetup-
%E5%88%86%E4%BA%AB-b5c55b78421f

6. 6
 Source: iThome，用資料科學發現與解決企業所不知道的各種問題
http://www.ithome.com.tw/tech/86091

7. 7
Data Engineer Software Engineer
Data Analyst
Data Scientist Domain Expert
 Source:經理人，「大數據」到底與我有什麼關聯
https://www.managertoday.com.tw/articles/view/52306

8. Roles in a Data Science Project
8
 Source:
https://www.slideshare.net/emcacademics/building-data-science-teams-31057129

9. When a new request has been
proposed?
New Request
» Discuss with users
» What is the motivation?
» How to deploy?
9

10. 呈現視覺化報表的步驟
10
Data
visualization
Data preprocess
Data cleaning
Understand
motivations and
Data
Make features
20%
30%
30%
20%

11. R
11

12. R or Python
» 主要用於統計分析、繪圖、
資料探勘
12
 Source: Python V.S R語言？在數據分析與挖掘方面該選哪一個？
https://www.itread01.com/articles/1475545480.html

13. Power BI
13

14. Power BI 14
 Source:
https://www.zhihu.com/question/35324481
Power BI
Service
Power BI Mobile
» Microsoft Excel add-ins: Power Query, Power Pivot, Power View and Power Map
» Microsoft released Power BI on 2015 and can be opened in Excel 2016
» Power BI is a suite of business analytics tools that deliver insights

15. 15
Power BI Report Server
(Free)

16. Example in Power BI Desktop
 [Sample] Retail Analysis
16
 Power BI 的零售分析範例教學
https://docs.microsoft.com/zh-tw/power-bi/sample-retail-analysis
Relationships Report

17. Power BI Visuals 17
 Source:
https://www.sqlbi.com/wp-
content/uploads/videotrainings/dashboarddesig
n/visuals-reference-may2017-A3.pdf

18. 情境
18

19. 情境
» 如果你有很多不同天的價錢資料, 要如何呈現
成視覺化圖表?
» 使用者需求? 製作報表的目的及目標?
19

20. Data you have
» Brand: ABC, XYZ
» Date: 2018/11/5~2018/11/30
» Model: A48a1, A50a2, A65a1, A65b1,
X43y2, A50x1, X50x4, X65x3
» Other attributes: Size, Type, Price, LAN
20

21. I. Understand motivations and Data
» 目的: 比較不同品牌的價錢變化
» 時間區間以天為單位? 以周為單位?
» 每一個Model, 每周有5~7個價錢, 要取最大值, 最小值,
還是平均值?
21
# of rows: 208
# of columns: 8

22. Import library {}
22
I. Understand motivations and Data
Import Data
» 比對字串是不是有符合正確的格式

23. Data cleaning
23

24. II. Data cleaning
24
Data type
» Text
» Date
» String
» Numeric
Data modeling
» Outlier
» Missing value
» Standardization

25. Regular expression 正則表示式
» 比對字串是不是有符合正確的格式
25
 [系列活動] Python爬蟲實戰
https://www.slideshare.net/tw_dsconf/python-78691041

26. II. Data cleaning
Model
» 將model_original清理成單純的model名稱 {library: magrittr, stringr, purrr}
26
分解步驟

27. 27
Model
» 將model_original清理成單純的model名稱 {library: magrittr, stringr, purrr}
II. Data cleaning

28. III. Data Preprocessing
28
Make features
» (1) 定義時間相關欄位
Date -> Week; Month ; Quarter [下頁範例]
Date -> Week; Week -> Month ; Month -> Quarter
» (2) 計算每周給個model的最高價, 最低價跟平均價

29. III. Data Preprocessing
29
Make features {library: magrittr, dplyr, lubridate}
» (1) 定義時間相關欄位
Date -> Week; Month ; Quarter [下列範例]
Date -> Week; Week -> Month ; Month -> Quarter

30. III. Data Preprocessing
30
Make features {library: magrittr, dplyr}
» (2) 計算每周給個model的最高價, 最低價跟平均價

31. III. Data Preprocessing
Write data {library: xlsx}
31

32. Data Visualization
32

33. IV. Data Visualization
» 匯入資料 [Get Data]
» 定義資料集 [Edit Queries]
» Visuals
 Scatter plot
 Scatter plot with animation
 Box and Whisker Chart
 Table
 Filter
33

34. 匯入資料 [Get Data]
34
Report
Data
Relationships
Pages

35. 匯入資料 [Get Data]
35

36. 定義資料集 [Edit Queries]
36

37. 定義資料集 [Edit Queries]
37

38. Visuals-Scatter plot
38

39. Visuals-Scatter plot with animation
39

40. Visuals-Box and Whisker Chart
40

41. Visuals-Filter
41

42. Visuals-Table
42

43. 43
Thank you~
Thank you!

44. References for Power BI
44

45. Start Using Power BI Desktop (1/2)
Download Power BI
Power BI Report Server
» Link: https://www.microsoft.com/en-
us/download/details.aspx?id=56723
» Plan to develop further
Power BI Desktop
» Link: https://www.microsoft.com/en-
us/download/details.aspx?id=45331
» Personal use and share files with others
» Update faster
45
Power BI Report Server

46. Start Using Power BI Desktop (2/2)
A. Conduct Reports
» 1. Choose visuals
» 2. Prepare your data to fit the data structure of
» visuals
» 3. Create relationships for several tables
» 4. Refer to official documents or on-line materials
B. Practice
» 1. Practice with samples (data pre-load)
» 2. Generate other visuals
» 3. Study how to add measures by DAX formula
» 4. Refer to official documents or on-line materials
46

47. More Visuals
47
» Rich visuals are available in marketplace

48. References for Power BI
48
» Power BI website
» https://powerbi.microsoft.com/zh-tw/desktop
» Power BI Desktop 使用者入門指南
» https://docs.microsoft.com/zh-tw/power-bi/desktop-getting-started
» Excel與Power BI
https://www.zhihu.com/question/35324481
» Power BI 的零售分析範例教學
» https://docs.microsoft.com/zh-tw/power-bi/sample-retail-analysis
» Power BI Visual Reference
https://www.sqlbi.com/wp-content/uploads/videotrainings/dashboarddesign/visuals-reference-
may2017-A3.pdf
» Power BI Desktop 影片
https://docs.microsoft.com/zh-tw/power-bi/desktop-videos
» Power BI Desktop 的 DAX 基本概念
https://docs.microsoft.com/zh-tw/power-bi/desktop-quickstart-learn-dax-basics